How can you evaluate how fast an algorithm works?

There are several ways to evaluate the speed of an algorithm:

1. Analysis of time complexity (Big O) — a theoretical estimate showing how the execution time of an algorithm grows with increasing input size.

2. Profiling and timing — practical assessment using tools and timing functions.

Example in Python using the time module to measure function execution time:

import time

def example_algorithm(n):
    total = 0
    for i in range(n):
        total += i
    return total

start_time = time.time()
example_algorithm(10**6)
end_time = time.time()
print(f"Execution time: {end_time - start_time} seconds")

3. Profilers — such as cProfile for Python, which allow detailed analysis of which parts of the code take the most time.

4. Testing on different input sizes — helps understand how the algorithm scales.

Thus, combining theoretical analysis and practical measurement provides a comprehensive understanding of the algorithm's performance.